A Survey on Application of Artificial Intelligence in Real Estate Industry Woubishet Zewdu Taffese Rehtorinpellonkatu 4B 405, 20500 Turku, Finland Tel: +358 40 8749981, E-mail:woubishet.taffese@gmail.com Abstract This paper will discuss the use of Artificial Intelligence (AI) in real estate industry. Today, besides Multiple Regression Analysis (MRA) models the use of AI systems for real estate valuation becomes better alternative. These AI systems for real estate valuation are more recent and becoming practical. Even if there are a number of artificial intelligent systems, Artificial Neural Networks (ANN) and Expert Systems (ES) are the ones presently applied for real estate valuation. Thus this paper will examine the current trends of ANN and ES and considers suitable applications in real estate valuation. In addition, prediction capability comparison between ANN and MRA will be presented by considering different case studies since both use statistical analysis and data modelling. Furthermore, common characteristic of ANN and ES will be compared. Beside ANN and ES, this paper will also discuss the application of hybrid systems for real estate valuation which mitigate the limitations and take advantage of the opportunities to produce systems that are more powerful than those that could be built with single intelligent systems. Keywords: Real Estate Valuation, Artificial Intelligence, Artificial Neural Networks, Expert Systems, Hybrid Systems Introduction An accurate and fast prediction of the real estate value is important to prospective homeowners, developers, investors, appraisers, tax assessors and other real estate market participants, such as, mortgage lenders and insurers. Real estate valuation based on traditional approaches such as cost and sale comparison approach lacks an accepted standard and a certification process. Therefore, the availability of a real estate value prediction model helps to fill up an important information gap and improve the efficiency of the real estate market [5]. Over the last two decades there has been a proliferation of empirical studies analyzing residential real estate values. The use of computer for real estate valuation began in the early 1980s, coinciding with the development of information systems technology. Subsequently, different statistical techniques were incorporated to process market data, among which the method of MRA proved especially relevant [14]. MRA models are the most popular quantitative technique in real estate valuation. It has been applied in various residential real estate valuations to assist appraiser in statistical analysis and complement the traditional sales comparison approach. MRA methods have experienced criticism from the academic and practitioner community. MRA has often produced serious problems for real estate valuation that primarily result from multicolinearity issues in the independent variables and from the inclusion of “outlier” properties in the sample. Moreover, nonlinearity within the data may make multiple regressions an inadequate model for a market that requires precise and fast responses. Nowadays, besides MRA models the use of AI systems for real estate valuation becomes better alternative. Using AI systems for real estate valuation is more recent and becoming practical. Since then there have been numerous experiences, and the creation of new models is on the increase. Even if there are a number of AI systems, ANN and ES are presently applied for real estate valuation. Artificial Neural Network An Artificial Neural Network (ANN), also called a Neural Network, is an interconnected group of artificial neurons that uses a mathematical or computational model for information processing based on a connectionist approach to computation. There is no precise agreed definition amongst researchers as to what an ANN is, but most would agree that it involves a network of relatively simple processing elements, where the global behaviour is determined by the connections between the processing elements and element parameters. The original inspiration for the technique was from examination of bioelectrical networks in the brain formed by neurons and their synapses. In an ANN model, simple nodes (called variously “neurons”, “neurodes”, “processing elements (PEs)” or “units”) are connected together to form a network of nodes hence the term "neural network”. ANNs usually have several layers. The first layer is called the input layer, the last one the output layer. The intermediate layers (if any) are called the hidden layers which can not be inspected from the outside. Example of simple ANN structure is shown in Figure 1. Figure 1 - Simple example of ANN The information to be analyzed is fed to the neurons of the first layer and then propagated to the neurons of the second layer for future processing. The result of this processing is then propagated to the next layer and so on until the last layer. Each unit receives some information from other units Input Layer Output Layer Output Inputs Hidden Layer Node (Neuron) Proceedings of the Third International Conference on Artificial Intelligence in Engineering & Technology November 22-24, 2006, Kota Kinabalu, Sabah, Malaysia 710